Overview

Dataset statistics

Number of variables81
Number of observations1451
Missing cells1370
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory918.3 KiB
Average record size in memory648.1 B

Variable types

Numeric29
Categorical52

Alerts

GarageYrBlt has constant value "0.0" Constant
df_index is highly correlated with IdHigh correlation
Id is highly correlated with df_indexHigh correlation
MSSubClass is highly correlated with BldgTypeHigh correlation
LandContour is highly correlated with LandSlopeHigh correlation
LandSlope is highly correlated with LandContourHigh correlation
BldgType is highly correlated with MSSubClassHigh correlation
HouseStyle is highly correlated with 2ndFlrSF and 1 other fieldsHigh correlation
OverallQual is highly correlated with YearBuilt and 8 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 11 other fieldsHigh correlation
YearRemodAdd is highly correlated with OverallQual and 8 other fieldsHigh correlation
Exterior1st is highly correlated with YearBuilt and 3 other fieldsHigh correlation
Exterior2nd is highly correlated with YearBuilt and 3 other fieldsHigh correlation
MasVnrType is highly correlated with MasVnrAreaHigh correlation
MasVnrArea is highly correlated with MasVnrTypeHigh correlation
ExterQual is highly correlated with OverallQual and 5 other fieldsHigh correlation
Foundation is highly correlated with OverallQual and 7 other fieldsHigh correlation
BsmtQual is highly correlated with YearBuiltHigh correlation
BsmtFinType1 is highly correlated with BsmtFinSF1High correlation
BsmtFinSF1 is highly correlated with BsmtFinType1 and 2 other fieldsHigh correlation
BsmtFinType2 is highly correlated with BsmtFinSF2High correlation
BsmtFinSF2 is highly correlated with BsmtFinType2High correlation
BsmtUnfSF is highly correlated with BsmtFinSF1High correlation
TotalBsmtSF is highly correlated with 1stFlrSF and 1 other fieldsHigh correlation
HeatingQC is highly correlated with YearBuilt and 2 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
2ndFlrSF is highly correlated with HouseStyle and 4 other fieldsHigh correlation
GrLivArea is highly correlated with OverallQual and 6 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 5 other fieldsHigh correlation
HalfBath is highly correlated with HouseStyle and 1 other fieldsHigh correlation
BedroomAbvGr is highly correlated with 2ndFlrSF and 2 other fieldsHigh correlation
KitchenQual is highly correlated with YearRemodAdd and 1 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with 2ndFlrSF and 4 other fieldsHigh correlation
Fireplaces is highly correlated with SalePriceHigh correlation
GarageCars is highly correlated with OverallQual and 5 other fieldsHigh correlation
GarageArea is highly correlated with OverallQual and 3 other fieldsHigh correlation
GarageQual is highly correlated with GarageCondHigh correlation
GarageCond is highly correlated with GarageQualHigh correlation
PoolArea is highly correlated with PoolQCHigh correlation
PoolQC is highly correlated with PoolAreaHigh correlation
MiscFeature is highly correlated with MiscValHigh correlation
MiscVal is highly correlated with MiscFeatureHigh correlation
SaleType is highly correlated with SaleConditionHigh correlation
SaleCondition is highly correlated with SaleTypeHigh correlation
SalePrice is highly correlated with OverallQual and 12 other fieldsHigh correlation
df_index is highly correlated with IdHigh correlation
Id is highly correlated with df_indexHigh correlation
MSSubClass is highly correlated with BldgTypeHigh correlation
LandContour is highly correlated with LandSlopeHigh correlation
LandSlope is highly correlated with LandContourHigh correlation
BldgType is highly correlated with MSSubClassHigh correlation
HouseStyle is highly correlated with 2ndFlrSF and 1 other fieldsHigh correlation
OverallQual is highly correlated with YearBuilt and 9 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 8 other fieldsHigh correlation
YearRemodAdd is highly correlated with OverallQual and 6 other fieldsHigh correlation
Exterior1st is highly correlated with YearBuilt and 2 other fieldsHigh correlation
Exterior2nd is highly correlated with YearBuilt and 2 other fieldsHigh correlation
MasVnrType is highly correlated with MasVnrAreaHigh correlation
MasVnrArea is highly correlated with MasVnrTypeHigh correlation
ExterQual is highly correlated with OverallQual and 4 other fieldsHigh correlation
Foundation is highly correlated with OverallQual and 6 other fieldsHigh correlation
BsmtQual is highly correlated with YearBuiltHigh correlation
BsmtFinSF1 is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
BsmtFinType2 is highly correlated with BsmtFinSF2High correlation
BsmtFinSF2 is highly correlated with BsmtFinType2High correlation
TotalBsmtSF is highly correlated with OverallQual and 3 other fieldsHigh correlation
HeatingQC is highly correlated with YearRemodAdd and 1 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 2 other fieldsHigh correlation
2ndFlrSF is highly correlated with HouseStyle and 4 other fieldsHigh correlation
GrLivArea is highly correlated with OverallQual and 6 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 3 other fieldsHigh correlation
HalfBath is highly correlated with HouseStyle and 1 other fieldsHigh correlation
BedroomAbvGr is highly correlated with 2ndFlrSF and 2 other fieldsHigh correlation
KitchenQual is highly correlated with YearRemodAdd and 1 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with 2ndFlrSF and 4 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 4 other fieldsHigh correlation
GarageArea is highly correlated with OverallQual and 2 other fieldsHigh correlation
GarageQual is highly correlated with GarageCondHigh correlation
GarageCond is highly correlated with GarageCars and 1 other fieldsHigh correlation
PoolArea is highly correlated with PoolQCHigh correlation
PoolQC is highly correlated with PoolAreaHigh correlation
SaleType is highly correlated with SaleConditionHigh correlation
SaleCondition is highly correlated with SaleTypeHigh correlation
SalePrice is highly correlated with OverallQual and 9 other fieldsHigh correlation
df_index is highly correlated with IdHigh correlation
Id is highly correlated with df_indexHigh correlation
MSSubClass is highly correlated with BldgTypeHigh correlation
LandContour is highly correlated with LandSlopeHigh correlation
LandSlope is highly correlated with LandContourHigh correlation
BldgType is highly correlated with MSSubClassHigh correlation
HouseStyle is highly correlated with 2ndFlrSF and 1 other fieldsHigh correlation
OverallQual is highly correlated with YearBuilt and 5 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 2 other fieldsHigh correlation
YearRemodAdd is highly correlated with YearBuiltHigh correlation
Exterior1st is highly correlated with Exterior2nd and 1 other fieldsHigh correlation
Exterior2nd is highly correlated with Exterior1st and 1 other fieldsHigh correlation
MasVnrType is highly correlated with MasVnrAreaHigh correlation
MasVnrArea is highly correlated with MasVnrTypeHigh correlation
ExterQual is highly correlated with OverallQual and 2 other fieldsHigh correlation
Foundation is highly correlated with OverallQual and 5 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtFullBathHigh correlation
BsmtFinType2 is highly correlated with BsmtFinSF2High correlation
BsmtFinSF2 is highly correlated with BsmtFinType2High correlation
TotalBsmtSF is highly correlated with 1stFlrSFHigh correlation
HeatingQC is highly correlated with FoundationHigh correlation
1stFlrSF is highly correlated with TotalBsmtSFHigh correlation
2ndFlrSF is highly correlated with HouseStyle and 2 other fieldsHigh correlation
GrLivArea is highly correlated with 2ndFlrSF and 3 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 2 other fieldsHigh correlation
HalfBath is highly correlated with HouseStyle and 1 other fieldsHigh correlation
BedroomAbvGr is highly correlated with TotRmsAbvGrdHigh correlation
KitchenQual is highly correlated with ExterQualHigh correlation
TotRmsAbvGrd is highly correlated with GrLivArea and 1 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 2 other fieldsHigh correlation
GarageArea is highly correlated with GarageCarsHigh correlation
GarageQual is highly correlated with GarageCondHigh correlation
GarageCond is highly correlated with GarageQualHigh correlation
PoolArea is highly correlated with PoolQCHigh correlation
PoolQC is highly correlated with PoolAreaHigh correlation
MiscFeature is highly correlated with MiscValHigh correlation
MiscVal is highly correlated with MiscFeatureHigh correlation
SaleType is highly correlated with SaleConditionHigh correlation
SaleCondition is highly correlated with SaleTypeHigh correlation
SalePrice is highly correlated with OverallQual and 3 other fieldsHigh correlation
BsmtFullBath is highly correlated with GarageYrBltHigh correlation
FullBath is highly correlated with GarageYrBltHigh correlation
BsmtCond is highly correlated with GarageYrBltHigh correlation
ExterQual is highly correlated with KitchenQual and 2 other fieldsHigh correlation
PoolQC is highly correlated with GarageYrBltHigh correlation
BsmtFinType1 is highly correlated with GarageYrBltHigh correlation
MasVnrType is highly correlated with GarageYrBltHigh correlation
GarageType is highly correlated with GarageYrBltHigh correlation
KitchenQual is highly correlated with ExterQual and 1 other fieldsHigh correlation
GarageCond is highly correlated with GarageYrBlt and 2 other fieldsHigh correlation
PavedDrive is highly correlated with GarageYrBltHigh correlation
Exterior2nd is highly correlated with GarageYrBlt and 2 other fieldsHigh correlation
FireplaceQu is highly correlated with GarageYrBltHigh correlation
SaleType is highly correlated with GarageYrBlt and 1 other fieldsHigh correlation
Utilities is highly correlated with GarageYrBltHigh correlation
KitchenAbvGr is highly correlated with GarageYrBltHigh correlation
Street is highly correlated with GarageYrBltHigh correlation
LotConfig is highly correlated with GarageYrBltHigh correlation
BsmtQual is highly correlated with GarageYrBltHigh correlation
GarageYrBlt is highly correlated with BsmtFullBath and 50 other fieldsHigh correlation
RoofStyle is highly correlated with GarageYrBltHigh correlation
CentralAir is highly correlated with GarageYrBltHigh correlation
BsmtHalfBath is highly correlated with GarageYrBltHigh correlation
Fireplaces is highly correlated with GarageYrBltHigh correlation
LandContour is highly correlated with GarageYrBlt and 1 other fieldsHigh correlation
BsmtExposure is highly correlated with GarageYrBltHigh correlation
Condition1 is highly correlated with GarageYrBltHigh correlation
BldgType is highly correlated with GarageYrBltHigh correlation
Fence is highly correlated with GarageYrBltHigh correlation
LotShape is highly correlated with GarageYrBltHigh correlation
HalfBath is highly correlated with GarageYrBlt and 1 other fieldsHigh correlation
Functional is highly correlated with GarageYrBltHigh correlation
GarageCars is highly correlated with GarageCond and 2 other fieldsHigh correlation
MiscFeature is highly correlated with GarageYrBltHigh correlation
Heating is highly correlated with GarageYrBltHigh correlation
LandSlope is highly correlated with GarageYrBlt and 1 other fieldsHigh correlation
YrSold is highly correlated with GarageYrBltHigh correlation
Condition2 is highly correlated with GarageYrBltHigh correlation
MSZoning is highly correlated with GarageYrBltHigh correlation
HouseStyle is highly correlated with GarageYrBlt and 1 other fieldsHigh correlation
Exterior1st is highly correlated with Exterior2nd and 2 other fieldsHigh correlation
ExterCond is highly correlated with GarageYrBltHigh correlation
BsmtFinType2 is highly correlated with GarageYrBltHigh correlation
Neighborhood is highly correlated with GarageYrBltHigh correlation
SaleCondition is highly correlated with SaleType and 1 other fieldsHigh correlation
RoofMatl is highly correlated with GarageYrBltHigh correlation
GarageFinish is highly correlated with GarageYrBltHigh correlation
Alley is highly correlated with GarageYrBltHigh correlation
Foundation is highly correlated with ExterQual and 4 other fieldsHigh correlation
GarageQual is highly correlated with GarageCond and 2 other fieldsHigh correlation
HeatingQC is highly correlated with GarageYrBlt and 1 other fieldsHigh correlation
Electrical is highly correlated with GarageYrBltHigh correlation
df_index is highly correlated with IdHigh correlation
Id is highly correlated with df_indexHigh correlation
MSSubClass is highly correlated with MSZoning and 12 other fieldsHigh correlation
MSZoning is highly correlated with MSSubClass and 2 other fieldsHigh correlation
Alley is highly correlated with MSZoningHigh correlation
LandContour is highly correlated with LandSlopeHigh correlation
LandSlope is highly correlated with LandContourHigh correlation
Neighborhood is highly correlated with Exterior1st and 1 other fieldsHigh correlation
BldgType is highly correlated with MSSubClass and 2 other fieldsHigh correlation
HouseStyle is highly correlated with MSSubClass and 4 other fieldsHigh correlation
OverallQual is highly correlated with OverallCond and 18 other fieldsHigh correlation
OverallCond is highly correlated with OverallQual and 1 other fieldsHigh correlation
YearBuilt is highly correlated with MSSubClass and 21 other fieldsHigh correlation
YearRemodAdd is highly correlated with YearBuilt and 9 other fieldsHigh correlation
Exterior1st is highly correlated with Neighborhood and 10 other fieldsHigh correlation
Exterior2nd is highly correlated with Neighborhood and 9 other fieldsHigh correlation
MasVnrType is highly correlated with MasVnrAreaHigh correlation
MasVnrArea is highly correlated with OverallQual and 4 other fieldsHigh correlation
ExterQual is highly correlated with MSSubClass and 12 other fieldsHigh correlation
Foundation is highly correlated with MSSubClass and 14 other fieldsHigh correlation
BsmtQual is highly correlated with OverallQual and 10 other fieldsHigh correlation
BsmtFinType1 is highly correlated with OverallQual and 7 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtFinType1 and 8 other fieldsHigh correlation
BsmtFinType2 is highly correlated with BsmtFinSF2High correlation
BsmtFinSF2 is highly correlated with BsmtFinType2High correlation
BsmtUnfSF is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
TotalBsmtSF is highly correlated with OverallQual and 7 other fieldsHigh correlation
Heating is highly correlated with CentralAirHigh correlation
HeatingQC is highly correlated with OverallQual and 9 other fieldsHigh correlation
CentralAir is highly correlated with YearBuilt and 3 other fieldsHigh correlation
Electrical is highly correlated with CentralAirHigh correlation
1stFlrSF is highly correlated with MSSubClass and 10 other fieldsHigh correlation
2ndFlrSF is highly correlated with MSSubClass and 9 other fieldsHigh correlation
GrLivArea is highly correlated with HouseStyle and 17 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinType1 and 3 other fieldsHigh correlation
FullBath is highly correlated with MSSubClass and 16 other fieldsHigh correlation
HalfBath is highly correlated with MSSubClass and 1 other fieldsHigh correlation
BedroomAbvGr is highly correlated with MSSubClass and 5 other fieldsHigh correlation
KitchenAbvGr is highly correlated with MSSubClass and 1 other fieldsHigh correlation
KitchenQual is highly correlated with OverallQual and 10 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with HouseStyle and 7 other fieldsHigh correlation
Fireplaces is highly correlated with 1stFlrSF and 2 other fieldsHigh correlation
FireplaceQu is highly correlated with FireplacesHigh correlation
GarageType is highly correlated with MSSubClass and 5 other fieldsHigh correlation
GarageFinish is highly correlated with GarageTypeHigh correlation
GarageCars is highly correlated with OverallQual and 8 other fieldsHigh correlation
GarageArea is highly correlated with OverallQual and 10 other fieldsHigh correlation
GarageQual is highly correlated with YearBuilt and 5 other fieldsHigh correlation
GarageCond is highly correlated with GarageType and 4 other fieldsHigh correlation
PavedDrive is highly correlated with YearBuiltHigh correlation
OpenPorchSF is highly correlated with BsmtFinSF1 and 1 other fieldsHigh correlation
EnclosedPorch is highly correlated with PoolAreaHigh correlation
PoolArea is highly correlated with TotalBsmtSF and 4 other fieldsHigh correlation
PoolQC is highly correlated with BsmtFinSF1 and 3 other fieldsHigh correlation
SaleType is highly correlated with YearRemodAdd and 1 other fieldsHigh correlation
SaleCondition is highly correlated with SaleTypeHigh correlation
SalePrice is highly correlated with OverallQual and 21 other fieldsHigh correlation
GarageYrBlt has 1370 (94.4%) missing values Missing
MiscVal is highly skewed (γ1 = 24.40151268) Skewed
df_index is uniformly distributed Uniform
Id is uniformly distributed Uniform
df_index has unique values Unique
Id has unique values Unique
MasVnrArea has 860 (59.3%) zeros Zeros
BsmtFinSF1 has 464 (32.0%) zeros Zeros
BsmtFinSF2 has 1284 (88.5%) zeros Zeros
BsmtUnfSF has 118 (8.1%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
2ndFlrSF has 824 (56.8%) zeros Zeros
LowQualFinSF has 1425 (98.2%) zeros Zeros
GarageArea has 81 (5.6%) zeros Zeros
WoodDeckSF has 755 (52.0%) zeros Zeros
OpenPorchSF has 653 (45.0%) zeros Zeros
EnclosedPorch has 1244 (85.7%) zeros Zeros
3SsnPorch has 1427 (98.3%) zeros Zeros
ScreenPorch has 1335 (92.0%) zeros Zeros
PoolArea has 1444 (99.5%) zeros Zeros
MiscVal has 1399 (96.4%) zeros Zeros

Reproduction

Analysis started2022-08-14 18:37:36.352284
Analysis finished2022-08-14 18:38:39.782564
Duration1 minute and 3.43 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1451
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean728.3742247
Minimum0
Maximum1459
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:39.817643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.5
Q1363.5
median728
Q31093.5
95-th percentile1386.5
Maximum1459
Range1459
Interquartile range (IQR)730

Descriptive statistics

Standard deviation421.7378576
Coefficient of variation (CV)0.5790126055
Kurtosis-1.199942419
Mean728.3742247
Median Absolute Deviation (MAD)365
Skewness0.00357607674
Sum1056871
Variance177862.8205
MonotonicityStrictly increasing
2022-08-14T18:38:39.885331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
9801
 
0.1%
9781
 
0.1%
9761
 
0.1%
9751
 
0.1%
9741
 
0.1%
9721
 
0.1%
9711
 
0.1%
9701
 
0.1%
9691
 
0.1%
Other values (1441)1441
99.3%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14541
0.1%
14531
0.1%
14521
0.1%
14511
0.1%
14501
0.1%

Id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1451
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean729.3742247
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:39.957868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.5
Q1364.5
median729
Q31094.5
95-th percentile1387.5
Maximum1460
Range1459
Interquartile range (IQR)730

Descriptive statistics

Standard deviation421.7378576
Coefficient of variation (CV)0.5782187571
Kurtosis-1.199942419
Mean729.3742247
Median Absolute Deviation (MAD)365
Skewness0.00357607674
Sum1058322
Variance177862.8205
MonotonicityStrictly increasing
2022-08-14T18:38:40.025586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
9811
 
0.1%
9791
 
0.1%
9771
 
0.1%
9761
 
0.1%
9751
 
0.1%
9731
 
0.1%
9721
 
0.1%
9711
 
0.1%
9701
 
0.1%
Other values (1441)1441
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
14601
0.1%
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14541
0.1%
14531
0.1%
14521
0.1%
14511
0.1%

MSSubClass
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.93314955
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:40.087931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.35036551
Coefficient of variation (CV)0.7438612802
Kurtosis1.577205972
Mean56.93314955
Median Absolute Deviation (MAD)30
Skewness1.408299254
Sum82610
Variance1793.553459
MonotonicityNot monotonic
2022-08-14T18:38:40.135303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20532
36.7%
60296
20.4%
50144
 
9.9%
12086
 
5.9%
3069
 
4.8%
16063
 
4.3%
7060
 
4.1%
8057
 
3.9%
9052
 
3.6%
19030
 
2.1%
Other values (5)62
 
4.3%
ValueCountFrequency (%)
20532
36.7%
3069
 
4.8%
404
 
0.3%
4512
 
0.8%
50144
 
9.9%
60296
20.4%
7060
 
4.1%
7516
 
1.1%
8057
 
3.9%
8520
 
1.4%
ValueCountFrequency (%)
19030
 
2.1%
18010
 
0.7%
16063
 
4.3%
12086
 
5.9%
9052
 
3.6%
8520
 
1.4%
8057
 
3.9%
7516
 
1.1%
7060
 
4.1%
60296
20.4%

MSZoning
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1145 
0.0
306 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01145
78.9%
0.0306
 
21.1%

Length

2022-08-14T18:38:40.189825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:40.243634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01145
78.9%
0.0306
 
21.1%

Most occurring characters

ValueCountFrequency (%)
01757
40.4%
.1451
33.3%
11145
26.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01757
60.5%
11145
39.5%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01757
40.4%
.1451
33.3%
11145
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01757
40.4%
.1451
33.3%
11145
26.3%

LotArea
Real number (ℝ≥0)

Distinct1066
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10507.80841
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:40.299214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3273
Q17537.5
median9477
Q311600
95-th percentile17308.5
Maximum215245
Range213945
Interquartile range (IQR)4062.5

Descriptive statistics

Standard deviation9992.987081
Coefficient of variation (CV)0.9510058323
Kurtosis203.5840787
Mean10507.80841
Median Absolute Deviation (MAD)2001
Skewness12.23574174
Sum15246830
Variance99859790.79
MonotonicityNot monotonic
2022-08-14T18:38:40.364964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720025
 
1.7%
960024
 
1.7%
600017
 
1.2%
840014
 
1.0%
900014
 
1.0%
1080014
 
1.0%
168010
 
0.7%
75009
 
0.6%
61208
 
0.6%
62408
 
0.6%
Other values (1056)1308
90.1%
ValueCountFrequency (%)
13001
 
0.1%
14771
 
0.1%
14911
 
0.1%
15261
 
0.1%
15332
 
0.1%
15961
 
0.1%
168010
0.7%
18691
 
0.1%
18902
 
0.1%
19201
 
0.1%
ValueCountFrequency (%)
2152451
0.1%
1646601
0.1%
1590001
0.1%
1151491
0.1%
707611
0.1%
638871
0.1%
572001
0.1%
535041
0.1%
532271
0.1%
531071
0.1%

Street
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1445 
0.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01445
99.6%
0.06
 
0.4%

Length

2022-08-14T18:38:40.425468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:40.477810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01445
99.6%
0.06
 
0.4%

Most occurring characters

ValueCountFrequency (%)
01457
33.5%
.1451
33.3%
11445
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01457
50.2%
11445
49.8%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01457
33.5%
.1451
33.3%
11445
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01457
33.5%
.1451
33.3%
11445
33.2%

Alley
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1361 
0.0
 
90

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01361
93.8%
0.090
 
6.2%

Length

2022-08-14T18:38:40.522086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:40.573976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01361
93.8%
0.090
 
6.2%

Most occurring characters

ValueCountFrequency (%)
01541
35.4%
.1451
33.3%
11361
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01541
53.1%
11361
46.9%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01541
35.4%
.1451
33.3%
11361
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01541
35.4%
.1451
33.3%
11361
31.3%

LotShape
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
969 
1.0
482 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0969
66.8%
1.0482
33.2%

Length

2022-08-14T18:38:40.617946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:40.671425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0969
66.8%
1.0482
33.2%

Most occurring characters

ValueCountFrequency (%)
02420
55.6%
.1451
33.3%
1482
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02420
83.4%
1482
 
16.6%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02420
55.6%
.1451
33.3%
1482
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02420
55.6%
.1451
33.3%
1482
 
11.1%

LandContour
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1302 
0.0
149 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01302
89.7%
0.0149
 
10.3%

Length

2022-08-14T18:38:40.718575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:40.771971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01302
89.7%
0.0149
 
10.3%

Most occurring characters

ValueCountFrequency (%)
01600
36.8%
.1451
33.3%
11302
29.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01600
55.1%
11302
44.9%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01600
36.8%
.1451
33.3%
11302
29.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01600
36.8%
.1451
33.3%
11302
29.9%

Utilities
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1450 
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01450
99.9%
0.01
 
0.1%

Length

2022-08-14T18:38:40.933501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:40.986261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01450
99.9%
0.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01452
33.4%
.1451
33.3%
11450
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01452
50.0%
11450
50.0%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01452
33.4%
.1451
33.3%
11450
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01452
33.4%
.1451
33.3%
11450
33.3%

LotConfig
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1045 
0.0
406 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.01045
72.0%
0.0406
 
28.0%

Length

2022-08-14T18:38:41.030625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:41.083631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01045
72.0%
0.0406
 
28.0%

Most occurring characters

ValueCountFrequency (%)
01857
42.7%
.1451
33.3%
11045
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01857
64.0%
11045
36.0%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01857
42.7%
.1451
33.3%
11045
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01857
42.7%
.1451
33.3%
11045
24.0%

LandSlope
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1373 
0.0
 
78

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01373
94.6%
0.078
 
5.4%

Length

2022-08-14T18:38:41.129156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:41.181167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01373
94.6%
0.078
 
5.4%

Most occurring characters

ValueCountFrequency (%)
01529
35.1%
.1451
33.3%
11373
31.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01529
52.7%
11373
47.3%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01529
35.1%
.1451
33.3%
11373
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01529
35.1%
.1451
33.3%
11373
31.5%

Neighborhood
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
1302 
1.0
149 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01302
89.7%
1.0149
 
10.3%

Length

2022-08-14T18:38:41.225475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:41.277935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01302
89.7%
1.0149
 
10.3%

Most occurring characters

ValueCountFrequency (%)
02753
63.2%
.1451
33.3%
1149
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02753
94.9%
1149
 
5.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02753
63.2%
.1451
33.3%
1149
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02753
63.2%
.1451
33.3%
1149
 
3.4%

Condition1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1251 
0.0
200 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01251
86.2%
0.0200
 
13.8%

Length

2022-08-14T18:38:41.323752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:41.376644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01251
86.2%
0.0200
 
13.8%

Most occurring characters

ValueCountFrequency (%)
01651
37.9%
.1451
33.3%
11251
28.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01651
56.9%
11251
43.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01651
37.9%
.1451
33.3%
11251
28.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01651
37.9%
.1451
33.3%
11251
28.7%

Condition2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1436 
0.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01436
99.0%
0.015
 
1.0%

Length

2022-08-14T18:38:41.422927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:41.475256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01436
99.0%
0.015
 
1.0%

Most occurring characters

ValueCountFrequency (%)
01466
33.7%
.1451
33.3%
11436
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01466
50.5%
11436
49.5%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01466
33.7%
.1451
33.3%
11436
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01466
33.7%
.1451
33.3%
11436
33.0%

BldgType
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1212 
0.0
239 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01212
83.5%
0.0239
 
16.5%

Length

2022-08-14T18:38:41.519524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:41.572164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01212
83.5%
0.0239
 
16.5%

Most occurring characters

ValueCountFrequency (%)
01690
38.8%
.1451
33.3%
11212
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01690
58.2%
11212
41.8%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01690
38.8%
.1451
33.3%
11212
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01690
38.8%
.1451
33.3%
11212
27.8%

HouseStyle
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
1009 
1.0
442 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01009
69.5%
1.0442
30.5%

Length

2022-08-14T18:38:41.618133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:41.671277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01009
69.5%
1.0442
30.5%

Most occurring characters

ValueCountFrequency (%)
02460
56.5%
.1451
33.3%
1442
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02460
84.8%
1442
 
15.2%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02460
56.5%
.1451
33.3%
1442
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02460
56.5%
.1451
33.3%
1442
 
10.2%

OverallQual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.093728463
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:41.713999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.381466719
Coefficient of variation (CV)0.226703032
Kurtosis0.08552729267
Mean6.093728463
Median Absolute Deviation (MAD)1
Skewness0.2134048765
Sum8842
Variance1.908450296
MonotonicityNot monotonic
2022-08-14T18:38:41.759275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5396
27.3%
6372
25.6%
7315
21.7%
8167
11.5%
4116
 
8.0%
943
 
3.0%
320
 
1.4%
1017
 
1.2%
23
 
0.2%
12
 
0.1%
ValueCountFrequency (%)
12
 
0.1%
23
 
0.2%
320
 
1.4%
4116
 
8.0%
5396
27.3%
6372
25.6%
7315
21.7%
8167
11.5%
943
 
3.0%
1017
 
1.2%
ValueCountFrequency (%)
1017
 
1.2%
943
 
3.0%
8167
11.5%
7315
21.7%
6372
25.6%
5396
27.3%
4116
 
8.0%
320
 
1.4%
23
 
0.2%
12
 
0.1%

OverallCond
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.579600276
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:41.806235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.113415425
Coefficient of variation (CV)0.1995511094
Kurtosis1.091941853
Mean5.579600276
Median Absolute Deviation (MAD)0
Skewness0.6939047475
Sum8096
Variance1.239693909
MonotonicityNot monotonic
2022-08-14T18:38:41.852961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5814
56.1%
6251
 
17.3%
7205
 
14.1%
872
 
5.0%
457
 
3.9%
324
 
1.7%
922
 
1.5%
25
 
0.3%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
25
 
0.3%
324
 
1.7%
457
 
3.9%
5814
56.1%
6251
 
17.3%
7205
 
14.1%
872
 
5.0%
922
 
1.5%
ValueCountFrequency (%)
922
 
1.5%
872
 
5.0%
7205
 
14.1%
6251
 
17.3%
5814
56.1%
457
 
3.9%
324
 
1.7%
25
 
0.3%
11
 
0.1%

YearBuilt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.09235
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:41.916450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1972
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.1902659
Coefficient of variation (CV)0.01531651518
Kurtosis-0.4423723285
Mean1971.09235
Median Absolute Deviation (MAD)25
Skewness-0.6082207534
Sum2860055
Variance911.4521552
MonotonicityNot monotonic
2022-08-14T18:38:41.982643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200664
 
4.4%
200564
 
4.4%
200454
 
3.7%
200747
 
3.2%
200344
 
3.0%
197633
 
2.3%
197732
 
2.2%
192030
 
2.1%
195926
 
1.8%
199825
 
1.7%
Other values (102)1032
71.1%
ValueCountFrequency (%)
18721
 
0.1%
18751
 
0.1%
18804
 
0.3%
18821
 
0.1%
18852
 
0.1%
18902
 
0.1%
18922
 
0.1%
18931
 
0.1%
18981
 
0.1%
190010
0.7%
ValueCountFrequency (%)
20101
 
0.1%
200918
 
1.2%
200823
 
1.6%
200747
3.2%
200664
4.4%
200564
4.4%
200454
3.7%
200344
3.0%
200221
 
1.4%
200120
 
1.4%

YearRemodAdd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.760165
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:42.053110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11966
median1993
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)38

Descriptive statistics

Standard deviation20.65133439
Coefficient of variation (CV)0.01040495207
Kurtosis-1.279511511
Mean1984.760165
Median Absolute Deviation (MAD)13
Skewness-0.4963389034
Sum2879887
Variance426.4776121
MonotonicityNot monotonic
2022-08-14T18:38:42.119863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950178
 
12.3%
200696
 
6.6%
200573
 
5.0%
200773
 
5.0%
200462
 
4.3%
200055
 
3.8%
200350
 
3.4%
200246
 
3.2%
200839
 
2.7%
199636
 
2.5%
Other values (51)743
51.2%
ValueCountFrequency (%)
1950178
12.3%
19514
 
0.3%
19525
 
0.3%
195310
 
0.7%
195414
 
1.0%
19559
 
0.6%
195610
 
0.7%
19579
 
0.6%
195815
 
1.0%
195918
 
1.2%
ValueCountFrequency (%)
20106
 
0.4%
200923
 
1.6%
200839
2.7%
200773
5.0%
200696
6.6%
200573
5.0%
200462
4.3%
200350
3.4%
200246
3.2%
200121
 
1.4%

RoofStyle
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1133 
0.0
318 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01133
78.1%
0.0318
 
21.9%

Length

2022-08-14T18:38:42.183293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:42.235744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01133
78.1%
0.0318
 
21.9%

Most occurring characters

ValueCountFrequency (%)
01769
40.6%
.1451
33.3%
11133
26.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01769
61.0%
11133
39.0%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01769
40.6%
.1451
33.3%
11133
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01769
40.6%
.1451
33.3%
11133
26.0%

RoofMatl
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1425 
0.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01425
98.2%
0.026
 
1.8%

Length

2022-08-14T18:38:42.281171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:42.333668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01425
98.2%
0.026
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01477
33.9%
.1451
33.3%
11425
32.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01477
50.9%
11425
49.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01477
33.9%
.1451
33.3%
11425
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01477
33.9%
.1451
33.3%
11425
32.7%

Exterior1st
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
942 
1.0
509 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0942
64.9%
1.0509
35.1%

Length

2022-08-14T18:38:42.377732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:42.431047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0942
64.9%
1.0509
35.1%

Most occurring characters

ValueCountFrequency (%)
02393
55.0%
.1451
33.3%
1509
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02393
82.5%
1509
 
17.5%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02393
55.0%
.1451
33.3%
1509
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02393
55.0%
.1451
33.3%
1509
 
11.7%

Exterior2nd
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
953 
1.0
498 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0953
65.7%
1.0498
34.3%

Length

2022-08-14T18:38:42.476145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:42.528758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0953
65.7%
1.0498
34.3%

Most occurring characters

ValueCountFrequency (%)
02404
55.2%
.1451
33.3%
1498
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02404
82.8%
1498
 
17.2%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02404
55.2%
.1451
33.3%
1498
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02404
55.2%
.1451
33.3%
1498
 
11.4%

MasVnrType
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
1006 
1.0
445 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01006
69.3%
1.0445
30.7%

Length

2022-08-14T18:38:42.573955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:42.626571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01006
69.3%
1.0445
30.7%

Most occurring characters

ValueCountFrequency (%)
02457
56.4%
.1451
33.3%
1445
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02457
84.7%
1445
 
15.3%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02457
56.4%
.1451
33.3%
1445
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02457
56.4%
.1451
33.3%
1445
 
10.2%

MasVnrArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct327
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.7567195
Minimum0
Maximum1600
Zeros860
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:42.680858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.1081504
Coefficient of variation (CV)1.745507677
Kurtosis10.07505004
Mean103.7567195
Median Absolute Deviation (MAD)0
Skewness2.668016481
Sum150551
Variance32800.16215
MonotonicityNot monotonic
2022-08-14T18:38:42.859150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0860
59.3%
1808
 
0.6%
728
 
0.6%
1088
 
0.6%
1207
 
0.5%
167
 
0.5%
2006
 
0.4%
3406
 
0.4%
1066
 
0.4%
806
 
0.4%
Other values (317)529
36.5%
ValueCountFrequency (%)
0860
59.3%
12
 
0.1%
111
 
0.1%
141
 
0.1%
167
 
0.5%
182
 
0.1%
221
 
0.1%
241
 
0.1%
271
 
0.1%
281
 
0.1%
ValueCountFrequency (%)
16001
0.1%
13781
0.1%
11701
0.1%
11291
0.1%
11151
0.1%
10471
0.1%
10311
0.1%
9751
0.1%
9221
0.1%
9211
0.1%

ExterQual
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
970 
1.0
481 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0970
66.9%
1.0481
33.1%

Length

2022-08-14T18:38:42.924885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:42.978076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0970
66.9%
1.0481
33.1%

Most occurring characters

ValueCountFrequency (%)
02421
55.6%
.1451
33.3%
1481
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02421
83.4%
1481
 
16.6%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02421
55.6%
.1451
33.3%
1481
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02421
55.6%
.1451
33.3%
1481
 
11.0%

ExterCond
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1273 
0.0
178 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01273
87.7%
0.0178
 
12.3%

Length

2022-08-14T18:38:43.023664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:43.076800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01273
87.7%
0.0178
 
12.3%

Most occurring characters

ValueCountFrequency (%)
01629
37.4%
.1451
33.3%
11273
29.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01629
56.1%
11273
43.9%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01629
37.4%
.1451
33.3%
11273
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01629
37.4%
.1451
33.3%
11273
29.2%

Foundation
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
813 
1.0
638 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0813
56.0%
1.0638
44.0%

Length

2022-08-14T18:38:43.124176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:43.177483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0813
56.0%
1.0638
44.0%

Most occurring characters

ValueCountFrequency (%)
02264
52.0%
.1451
33.3%
1638
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02264
78.0%
1638
 
22.0%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02264
52.0%
.1451
33.3%
1638
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02264
52.0%
.1451
33.3%
1638
 
14.7%

BsmtQual
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
840 
1.0
611 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0840
57.9%
1.0611
42.1%

Length

2022-08-14T18:38:43.223618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:43.276603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0840
57.9%
1.0611
42.1%

Most occurring characters

ValueCountFrequency (%)
02291
52.6%
.1451
33.3%
1611
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02291
78.9%
1611
 
21.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02291
52.6%
.1451
33.3%
1611
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02291
52.6%
.1451
33.3%
1611
 
14.0%

BsmtCond
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1303 
0.0
148 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01303
89.8%
0.0148
 
10.2%

Length

2022-08-14T18:38:43.322804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:43.375162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01303
89.8%
0.0148
 
10.2%

Most occurring characters

ValueCountFrequency (%)
01599
36.7%
.1451
33.3%
11303
29.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01599
55.1%
11303
44.9%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01599
36.7%
.1451
33.3%
11303
29.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01599
36.7%
.1451
33.3%
11303
29.9%

BsmtExposure
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
1337 
1.0
 
114

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01337
92.1%
1.0114
 
7.9%

Length

2022-08-14T18:38:43.421061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:43.473664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01337
92.1%
1.0114
 
7.9%

Most occurring characters

ValueCountFrequency (%)
02788
64.0%
.1451
33.3%
1114
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02788
96.1%
1114
 
3.9%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02788
64.0%
.1451
33.3%
1114
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02788
64.0%
.1451
33.3%
1114
 
2.6%

BsmtFinType1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
1038 
1.0
413 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01038
71.5%
1.0413
 
28.5%

Length

2022-08-14T18:38:43.517558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:43.570304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01038
71.5%
1.0413
 
28.5%

Most occurring characters

ValueCountFrequency (%)
02489
57.2%
.1451
33.3%
1413
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02489
85.8%
1413
 
14.2%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02489
57.2%
.1451
33.3%
1413
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02489
57.2%
.1451
33.3%
1413
 
9.5%

BsmtFinSF1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct633
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.2749828
Minimum0
Maximum5644
Zeros464
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:43.623785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median381
Q3707
95-th percentile1272
Maximum5644
Range5644
Interquartile range (IQR)707

Descriptive statistics

Standard deviation455.3692759
Coefficient of variation (CV)1.029606678
Kurtosis11.29369587
Mean442.2749828
Median Absolute Deviation (MAD)381
Skewness1.702582099
Sum641741
Variance207361.1774
MonotonicityNot monotonic
2022-08-14T18:38:43.685958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0464
32.0%
2412
 
0.8%
169
 
0.6%
6165
 
0.3%
205
 
0.3%
9365
 
0.3%
6625
 
0.3%
6865
 
0.3%
6974
 
0.3%
6414
 
0.3%
Other values (623)933
64.3%
ValueCountFrequency (%)
0464
32.0%
21
 
0.1%
169
 
0.6%
205
 
0.3%
2412
 
0.8%
251
 
0.1%
271
 
0.1%
283
 
0.2%
331
 
0.1%
351
 
0.1%
ValueCountFrequency (%)
56441
0.1%
22601
0.1%
21881
0.1%
20961
0.1%
19041
0.1%
18801
0.1%
18101
0.1%
17671
0.1%
17211
0.1%
16961
0.1%

BsmtFinType2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1247 
0.0
204 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01247
85.9%
0.0204
 
14.1%

Length

2022-08-14T18:38:43.745960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:43.798527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01247
85.9%
0.0204
 
14.1%

Most occurring characters

ValueCountFrequency (%)
01655
38.0%
.1451
33.3%
11247
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01655
57.0%
11247
43.0%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01655
38.0%
.1451
33.3%
11247
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01655
38.0%
.1451
33.3%
11247
28.6%

BsmtFinSF2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.83804273
Minimum0
Maximum1474
Zeros1284
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:43.851866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile398
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.7773245
Coefficient of variation (CV)3.453972776
Kurtosis19.96462251
Mean46.83804273
Median Absolute Deviation (MAD)0
Skewness4.240229558
Sum67962
Variance26171.90272
MonotonicityNot monotonic
2022-08-14T18:38:43.918494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01284
88.5%
1805
 
0.3%
3743
 
0.2%
5512
 
0.1%
1472
 
0.1%
2942
 
0.1%
3912
 
0.1%
5392
 
0.1%
962
 
0.1%
4802
 
0.1%
Other values (134)145
 
10.0%
ValueCountFrequency (%)
01284
88.5%
281
 
0.1%
321
 
0.1%
351
 
0.1%
401
 
0.1%
412
 
0.1%
642
 
0.1%
681
 
0.1%
801
 
0.1%
811
 
0.1%
ValueCountFrequency (%)
14741
0.1%
11271
0.1%
11201
0.1%
10851
0.1%
10801
0.1%
10631
0.1%
10611
0.1%
10571
0.1%
10311
0.1%
10291
0.1%

BsmtUnfSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct777
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.1971054
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:43.988223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1222
median479
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)586

Descriptive statistics

Standard deviation442.2091919
Coefficient of variation (CV)0.7796393664
Kurtosis0.4751190376
Mean567.1971054
Median Absolute Deviation (MAD)289
Skewness0.9199767396
Sum823003
Variance195548.9694
MonotonicityNot monotonic
2022-08-14T18:38:44.054803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0118
 
8.1%
7289
 
0.6%
3007
 
0.5%
3847
 
0.5%
5727
 
0.5%
6007
 
0.5%
4406
 
0.4%
2706
 
0.4%
2806
 
0.4%
6726
 
0.4%
Other values (767)1272
87.7%
ValueCountFrequency (%)
0118
8.1%
141
 
0.1%
151
 
0.1%
232
 
0.1%
261
 
0.1%
291
 
0.1%
301
 
0.1%
322
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
23361
0.1%
21531
0.1%
21211
0.1%
20461
0.1%
20421
0.1%
20021
0.1%
19691
0.1%
19351
0.1%
19261
0.1%
19071
0.1%

TotalBsmtSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct717
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1056.310131
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:44.122428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile520
Q1795
median991
Q31297.5
95-th percentile1749
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation437.9146086
Coefficient of variation (CV)0.4145701114
Kurtosis13.44412043
Mean1056.310131
Median Absolute Deviation (MAD)234
Skewness1.535566317
Sum1532706
Variance191769.2044
MonotonicityNot monotonic
2022-08-14T18:38:44.185663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
2.5%
86435
 
2.4%
67217
 
1.2%
91215
 
1.0%
104014
 
1.0%
81613
 
0.9%
76812
 
0.8%
72812
 
0.8%
89411
 
0.8%
84811
 
0.8%
Other values (707)1274
87.8%
ValueCountFrequency (%)
037
2.5%
1051
 
0.1%
1901
 
0.1%
2643
 
0.2%
2701
 
0.1%
2901
 
0.1%
3191
 
0.1%
3601
 
0.1%
3721
 
0.1%
3846
 
0.4%
ValueCountFrequency (%)
61101
0.1%
32061
0.1%
32001
0.1%
31381
0.1%
30941
0.1%
26331
0.1%
25241
0.1%
24441
0.1%
23961
0.1%
23921
0.1%

Heating
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1419 
0.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01419
97.8%
0.032
 
2.2%

Length

2022-08-14T18:38:44.245946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:44.297771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01419
97.8%
0.032
 
2.2%

Most occurring characters

ValueCountFrequency (%)
01483
34.1%
.1451
33.3%
11419
32.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01483
51.1%
11419
48.9%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01483
34.1%
.1451
33.3%
11419
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01483
34.1%
.1451
33.3%
11419
32.6%

HeatingQC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
734 
0.0
717 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0734
50.6%
0.0717
49.4%

Length

2022-08-14T18:38:44.341392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:44.394068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0734
50.6%
0.0717
49.4%

Most occurring characters

ValueCountFrequency (%)
02168
49.8%
.1451
33.3%
1734
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02168
74.7%
1734
 
25.3%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02168
49.8%
.1451
33.3%
1734
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02168
49.8%
.1451
33.3%
1734
 
16.9%

CentralAir
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1356 
0.0
 
95

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01356
93.5%
0.095
 
6.5%

Length

2022-08-14T18:38:44.439936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:44.492249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01356
93.5%
0.095
 
6.5%

Most occurring characters

ValueCountFrequency (%)
01546
35.5%
.1451
33.3%
11356
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01546
53.3%
11356
46.7%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01546
35.5%
.1451
33.3%
11356
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01546
35.5%
.1451
33.3%
11356
31.2%

Electrical
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1326 
0.0
 
125

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01326
91.4%
0.0125
 
8.6%

Length

2022-08-14T18:38:44.536273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:44.705606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01326
91.4%
0.0125
 
8.6%

Most occurring characters

ValueCountFrequency (%)
01576
36.2%
.1451
33.3%
11326
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01576
54.3%
11326
45.7%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01576
36.2%
.1451
33.3%
11326
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01576
36.2%
.1451
33.3%
11326
30.5%

1stFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct748
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161.551344
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:44.759603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.5
Q1882
median1086
Q31391
95-th percentile1827
Maximum4692
Range4358
Interquartile range (IQR)509

Descriptive statistics

Standard deviation385.0025319
Coefficient of variation (CV)0.3314554574
Kurtosis5.832535396
Mean1161.551344
Median Absolute Deviation (MAD)234
Skewness1.373141509
Sum1685411
Variance148226.9496
MonotonicityNot monotonic
2022-08-14T18:38:44.823625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86425
 
1.7%
104016
 
1.1%
91214
 
1.0%
89412
 
0.8%
84812
 
0.8%
67211
 
0.8%
8169
 
0.6%
6309
 
0.6%
9607
 
0.5%
8327
 
0.5%
Other values (738)1329
91.6%
ValueCountFrequency (%)
3341
 
0.1%
3721
 
0.1%
4381
 
0.1%
4801
 
0.1%
4837
0.5%
4951
 
0.1%
5205
0.3%
5251
 
0.1%
5261
 
0.1%
5361
 
0.1%
ValueCountFrequency (%)
46921
0.1%
32281
0.1%
31381
0.1%
28981
0.1%
26331
0.1%
25241
0.1%
24441
0.1%
24111
0.1%
24021
0.1%
23921
0.1%

2ndFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct414
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.7422467
Minimum0
Maximum2065
Zeros824
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:44.891779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.5
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.4532772
Coefficient of variation (CV)1.258725411
Kurtosis-0.5452797813
Mean346.7422467
Median Absolute Deviation (MAD)0
Skewness0.8157663449
Sum503123
Variance190491.4632
MonotonicityNot monotonic
2022-08-14T18:38:44.958704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0824
56.8%
72810
 
0.7%
5049
 
0.6%
5468
 
0.6%
6728
 
0.6%
7207
 
0.5%
6007
 
0.5%
8966
 
0.4%
7565
 
0.3%
6895
 
0.3%
Other values (404)562
38.7%
ValueCountFrequency (%)
0824
56.8%
1101
 
0.1%
1671
 
0.1%
1921
 
0.1%
2081
 
0.1%
2131
 
0.1%
2201
 
0.1%
2241
 
0.1%
2402
 
0.1%
2522
 
0.1%
ValueCountFrequency (%)
20651
0.1%
18721
0.1%
18181
0.1%
17961
0.1%
16111
0.1%
15891
0.1%
15401
0.1%
15381
0.1%
15231
0.1%
15191
0.1%

LowQualFinSF
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.880771881
Minimum0
Maximum572
Zeros1425
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:45.019675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.77156021
Coefficient of variation (CV)8.293394335
Kurtosis82.69356902
Mean5.880771881
Median Absolute Deviation (MAD)0
Skewness8.982567342
Sum8533
Variance2378.665085
MonotonicityNot monotonic
2022-08-14T18:38:45.072124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
01425
98.2%
803
 
0.2%
3602
 
0.1%
2051
 
0.1%
4791
 
0.1%
3971
 
0.1%
5141
 
0.1%
1201
 
0.1%
4811
 
0.1%
2321
 
0.1%
Other values (14)14
 
1.0%
ValueCountFrequency (%)
01425
98.2%
531
 
0.1%
803
 
0.2%
1201
 
0.1%
1441
 
0.1%
1561
 
0.1%
2051
 
0.1%
2321
 
0.1%
2341
 
0.1%
3602
 
0.1%
ValueCountFrequency (%)
5721
0.1%
5281
0.1%
5151
0.1%
5141
0.1%
5131
0.1%
4811
0.1%
4791
0.1%
4731
0.1%
4201
0.1%
3971
0.1%

GrLivArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct858
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1514.174363
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:45.134964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11128
median1464
Q31776
95-th percentile2464
Maximum5642
Range5308
Interquartile range (IQR)648

Descriptive statistics

Standard deviation525.7995205
Coefficient of variation (CV)0.3472516333
Kurtosis4.927435796
Mean1514.174363
Median Absolute Deviation (MAD)326
Skewness1.373512412
Sum2197067
Variance276465.1358
MonotonicityNot monotonic
2022-08-14T18:38:45.202244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86422
 
1.5%
104014
 
1.0%
89411
 
0.8%
84810
 
0.7%
145610
 
0.7%
9129
 
0.6%
10928
 
0.6%
12008
 
0.6%
8168
 
0.6%
9877
 
0.5%
Other values (848)1344
92.6%
ValueCountFrequency (%)
3341
 
0.1%
4381
 
0.1%
4801
 
0.1%
5201
 
0.1%
6051
 
0.1%
6161
 
0.1%
6306
0.4%
6722
 
0.1%
6911
 
0.1%
6931
 
0.1%
ValueCountFrequency (%)
56421
0.1%
46761
0.1%
44761
0.1%
43161
0.1%
36271
0.1%
36081
0.1%
34931
0.1%
34471
0.1%
33951
0.1%
32791
0.1%

BsmtFullBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
853 
1.0
582 
2.0
 
15
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0853
58.8%
1.0582
40.1%
2.015
 
1.0%
3.01
 
0.1%

Length

2022-08-14T18:38:45.262098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:45.317408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0853
58.8%
1.0582
40.1%
2.015
 
1.0%
3.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02304
52.9%
.1451
33.3%
1582
 
13.4%
215
 
0.3%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02304
79.4%
1582
 
20.1%
215
 
0.5%
31
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02304
52.9%
.1451
33.3%
1582
 
13.4%
215
 
0.3%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02304
52.9%
.1451
33.3%
1582
 
13.4%
215
 
0.3%
31
 
< 0.1%

BsmtHalfBath
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
1369 
1.0
 
80
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01369
94.3%
1.080
 
5.5%
2.02
 
0.1%

Length

2022-08-14T18:38:45.366457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:45.420220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01369
94.3%
1.080
 
5.5%
2.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02820
64.8%
.1451
33.3%
180
 
1.8%
22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02820
97.2%
180
 
2.8%
22
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02820
64.8%
.1451
33.3%
180
 
1.8%
22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02820
64.8%
.1451
33.3%
180
 
1.8%
22
 
< 0.1%

FullBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2.0
761 
1.0
649 
3.0
 
32
0.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0761
52.4%
1.0649
44.7%
3.032
 
2.2%
0.09
 
0.6%

Length

2022-08-14T18:38:45.466622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:45.521872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0761
52.4%
1.0649
44.7%
3.032
 
2.2%
0.09
 
0.6%

Most occurring characters

ValueCountFrequency (%)
01460
33.5%
.1451
33.3%
2761
17.5%
1649
14.9%
332
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01460
50.3%
2761
26.2%
1649
22.4%
332
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01460
33.5%
.1451
33.3%
2761
17.5%
1649
14.9%
332
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01460
33.5%
.1451
33.3%
2761
17.5%
1649
14.9%
332
 
0.7%

HalfBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
910 
1.0
529 
2.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0910
62.7%
1.0529
36.5%
2.012
 
0.8%

Length

2022-08-14T18:38:45.571097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:45.625463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0910
62.7%
1.0529
36.5%
2.012
 
0.8%

Most occurring characters

ValueCountFrequency (%)
02361
54.2%
.1451
33.3%
1529
 
12.2%
212
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02361
81.4%
1529
 
18.2%
212
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02361
54.2%
.1451
33.3%
1529
 
12.2%
212
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02361
54.2%
.1451
33.3%
1529
 
12.2%
212
 
0.3%

BedroomAbvGr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866988284
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:45.668967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8150856688
Coefficient of variation (CV)0.2843003138
Kurtosis2.254820679
Mean2.866988284
Median Absolute Deviation (MAD)0
Skewness0.2178468767
Sum4160
Variance0.6643646474
MonotonicityNot monotonic
2022-08-14T18:38:45.715978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3800
55.1%
2356
24.5%
4211
 
14.5%
149
 
3.4%
521
 
1.4%
67
 
0.5%
06
 
0.4%
81
 
0.1%
ValueCountFrequency (%)
06
 
0.4%
149
 
3.4%
2356
24.5%
3800
55.1%
4211
 
14.5%
521
 
1.4%
67
 
0.5%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
67
 
0.5%
521
 
1.4%
4211
 
14.5%
3800
55.1%
2356
24.5%
149
 
3.4%
06
 
0.4%

KitchenAbvGr
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1384 
2.0
 
64
3.0
 
2
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01384
95.4%
2.064
 
4.4%
3.02
 
0.1%
0.01
 
0.1%

Length

2022-08-14T18:38:45.770817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:45.824909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01384
95.4%
2.064
 
4.4%
3.02
 
0.1%
0.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01452
33.4%
.1451
33.3%
11384
31.8%
264
 
1.5%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01452
50.0%
11384
47.7%
264
 
2.2%
32
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01452
33.4%
.1451
33.3%
11384
31.8%
264
 
1.5%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01452
33.4%
.1451
33.3%
11384
31.8%
264
 
1.5%
32
 
< 0.1%

KitchenQual
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
872 
1.0
579 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0872
60.1%
1.0579
39.9%

Length

2022-08-14T18:38:45.872841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:45.925941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0872
60.1%
1.0579
39.9%

Most occurring characters

ValueCountFrequency (%)
02323
53.4%
.1451
33.3%
1579
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02323
80.0%
1579
 
20.0%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02323
53.4%
.1451
33.3%
1579
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02323
53.4%
.1451
33.3%
1579
 
13.3%

TotRmsAbvGrd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.516884907
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:45.966912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.626575908
Coefficient of variation (CV)0.2495940824
Kurtosis0.8863725364
Mean6.516884907
Median Absolute Deviation (MAD)1
Skewness0.6808626334
Sum9456
Variance2.645749186
MonotonicityNot monotonic
2022-08-14T18:38:46.017550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6401
27.6%
7325
22.4%
5274
18.9%
8186
12.8%
496
 
6.6%
974
 
5.1%
1047
 
3.2%
1118
 
1.2%
317
 
1.2%
1211
 
0.8%
Other values (2)2
 
0.1%
ValueCountFrequency (%)
21
 
0.1%
317
 
1.2%
496
 
6.6%
5274
18.9%
6401
27.6%
7325
22.4%
8186
12.8%
974
 
5.1%
1047
 
3.2%
1118
 
1.2%
ValueCountFrequency (%)
141
 
0.1%
1211
 
0.8%
1118
 
1.2%
1047
 
3.2%
974
 
5.1%
8186
12.8%
7325
22.4%
6401
27.6%
5274
18.9%
496
 
6.6%

Functional
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1352 
0.0
 
99

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01352
93.2%
0.099
 
6.8%

Length

2022-08-14T18:38:46.072459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:46.124432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01352
93.2%
0.099
 
6.8%

Most occurring characters

ValueCountFrequency (%)
01550
35.6%
.1451
33.3%
11352
31.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01550
53.4%
11352
46.6%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01550
35.6%
.1451
33.3%
11352
31.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01550
35.6%
.1451
33.3%
11352
31.1%

Fireplaces
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
685 
1.0
648 
2.0
113 
3.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0685
47.2%
1.0648
44.7%
2.0113
 
7.8%
3.05
 
0.3%

Length

2022-08-14T18:38:46.168266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:46.223294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0685
47.2%
1.0648
44.7%
2.0113
 
7.8%
3.05
 
0.3%

Most occurring characters

ValueCountFrequency (%)
02136
49.1%
.1451
33.3%
1648
 
14.9%
2113
 
2.6%
35
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02136
73.6%
1648
 
22.3%
2113
 
3.9%
35
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02136
49.1%
.1451
33.3%
1648
 
14.9%
2113
 
2.6%
35
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02136
49.1%
.1451
33.3%
1648
 
14.9%
2113
 
2.6%
35
 
0.1%

FireplaceQu
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
1140 
1.0
311 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01140
78.6%
1.0311
 
21.4%

Length

2022-08-14T18:38:46.272562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:46.325100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01140
78.6%
1.0311
 
21.4%

Most occurring characters

ValueCountFrequency (%)
02591
59.5%
.1451
33.3%
1311
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02591
89.3%
1311
 
10.7%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02591
59.5%
.1451
33.3%
1311
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02591
59.5%
.1451
33.3%
1311
 
7.1%

GarageType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
863 
0.0
588 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0863
59.5%
0.0588
40.5%

Length

2022-08-14T18:38:46.370787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:46.424022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0863
59.5%
0.0588
40.5%

Most occurring characters

ValueCountFrequency (%)
02039
46.8%
.1451
33.3%
1863
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02039
70.3%
1863
29.7%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02039
46.8%
.1451
33.3%
1863
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02039
46.8%
.1451
33.3%
1863
19.8%

GarageYrBlt
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)1.2%
Missing1370
Missing (%)94.4%
Memory size11.5 KiB
0.0
81 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters243
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.081
 
5.6%
(Missing)1370
94.4%

Length

2022-08-14T18:38:46.587155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:46.639792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.081
100.0%

Most occurring characters

ValueCountFrequency (%)
0162
66.7%
.81
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number162
66.7%
Other Punctuation81
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0162
100.0%
Other Punctuation
ValueCountFrequency (%)
.81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common243
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0162
66.7%
.81
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0162
66.7%
.81
33.3%

GarageFinish
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0.0
1033 
1.0
418 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01033
71.2%
1.0418
28.8%

Length

2022-08-14T18:38:46.682832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:46.735497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01033
71.2%
1.0418
28.8%

Most occurring characters

ValueCountFrequency (%)
02484
57.1%
.1451
33.3%
1418
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02484
85.6%
1418
 
14.4%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02484
57.1%
.1451
33.3%
1418
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02484
57.1%
.1451
33.3%
1418
 
9.6%

GarageCars
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2.0
816 
1.0
369 
3.0
180 
0.0
 
81
4.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0816
56.2%
1.0369
25.4%
3.0180
 
12.4%
0.081
 
5.6%
4.05
 
0.3%

Length

2022-08-14T18:38:46.780655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:46.838055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0816
56.2%
1.0369
25.4%
3.0180
 
12.4%
0.081
 
5.6%
4.05
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01532
35.2%
.1451
33.3%
2816
18.7%
1369
 
8.5%
3180
 
4.1%
45
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01532
52.8%
2816
28.1%
1369
 
12.7%
3180
 
6.2%
45
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01532
35.2%
.1451
33.3%
2816
18.7%
1369
 
8.5%
3180
 
4.1%
45
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01532
35.2%
.1451
33.3%
2816
18.7%
1369
 
8.5%
3180
 
4.1%
45
 
0.1%

GarageArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct438
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.5251551
Minimum0
Maximum1418
Zeros81
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:46.897724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1327.5
median478
Q3576
95-th percentile849
Maximum1418
Range1418
Interquartile range (IQR)248.5

Descriptive statistics

Standard deviation214.1717516
Coefficient of variation (CV)0.4532494182
Kurtosis0.9092646116
Mean472.5251551
Median Absolute Deviation (MAD)118
Skewness0.1825849828
Sum685634
Variance45869.53919
MonotonicityNot monotonic
2022-08-14T18:38:46.962170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081
 
5.6%
44048
 
3.3%
57647
 
3.2%
24038
 
2.6%
48433
 
2.3%
52833
 
2.3%
28827
 
1.9%
40024
 
1.7%
26424
 
1.7%
48023
 
1.6%
Other values (428)1073
73.9%
ValueCountFrequency (%)
081
5.6%
1602
 
0.1%
1641
 
0.1%
1809
 
0.6%
1861
 
0.1%
1891
 
0.1%
1921
 
0.1%
1981
 
0.1%
2004
 
0.3%
2053
 
0.2%
ValueCountFrequency (%)
14181
0.1%
13901
0.1%
13561
0.1%
12481
0.1%
12201
0.1%
11661
0.1%
11341
0.1%
10691
0.1%
10531
0.1%
10522
0.1%

GarageQual
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1302 
0.0
149 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01302
89.7%
0.0149
 
10.3%

Length

2022-08-14T18:38:47.024568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:47.077893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01302
89.7%
0.0149
 
10.3%

Most occurring characters

ValueCountFrequency (%)
01600
36.8%
.1451
33.3%
11302
29.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01600
55.1%
11302
44.9%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01600
36.8%
.1451
33.3%
11302
29.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01600
36.8%
.1451
33.3%
11302
29.9%

GarageCond
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1317 
0.0
134 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01317
90.8%
0.0134
 
9.2%

Length

2022-08-14T18:38:47.123669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:47.176665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01317
90.8%
0.0134
 
9.2%

Most occurring characters

ValueCountFrequency (%)
01585
36.4%
.1451
33.3%
11317
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01585
54.6%
11317
45.4%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01585
36.4%
.1451
33.3%
11317
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01585
36.4%
.1451
33.3%
11317
30.3%

PavedDrive
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1331 
0.0
 
90
2.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01331
91.7%
0.090
 
6.2%
2.030
 
2.1%

Length

2022-08-14T18:38:47.222386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:47.276451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01331
91.7%
0.090
 
6.2%
2.030
 
2.1%

Most occurring characters

ValueCountFrequency (%)
01541
35.4%
.1451
33.3%
11331
30.6%
230
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01541
53.1%
11331
45.9%
230
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01541
35.4%
.1451
33.3%
11331
30.6%
230
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01541
35.4%
.1451
33.3%
11331
30.6%
230
 
0.7%

WoodDeckSF
Real number (ℝ≥0)

ZEROS

Distinct274
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.41281875
Minimum0
Maximum857
Zeros755
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:47.331164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.4368524
Coefficient of variation (CV)1.328599803
Kurtosis2.997259286
Mean94.41281875
Median Absolute Deviation (MAD)0
Skewness1.541871617
Sum136993
Variance15734.40395
MonotonicityNot monotonic
2022-08-14T18:38:47.396589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0755
52.0%
19238
 
2.6%
10035
 
2.4%
14433
 
2.3%
12031
 
2.1%
16828
 
1.9%
14015
 
1.0%
22414
 
1.0%
24010
 
0.7%
20810
 
0.7%
Other values (264)482
33.2%
ValueCountFrequency (%)
0755
52.0%
122
 
0.1%
242
 
0.1%
262
 
0.1%
282
 
0.1%
301
 
0.1%
321
 
0.1%
331
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
8571
0.1%
7361
0.1%
7281
0.1%
6701
0.1%
6681
0.1%
6351
0.1%
5861
0.1%
5761
0.1%
5741
0.1%
5501
0.1%

OpenPorchSF
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct201
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.42246726
Minimum0
Maximum547
Zeros653
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:47.463977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q368
95-th percentile173
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.06015037
Coefficient of variation (CV)1.423021099
Kurtosis8.664993445
Mean46.42246726
Median Absolute Deviation (MAD)24
Skewness2.384908285
Sum67359
Variance4363.943467
MonotonicityNot monotonic
2022-08-14T18:38:47.526756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0653
45.0%
3629
 
2.0%
4821
 
1.4%
2021
 
1.4%
4019
 
1.3%
4519
 
1.3%
2416
 
1.1%
3016
 
1.1%
6015
 
1.0%
3914
 
1.0%
Other values (191)628
43.3%
ValueCountFrequency (%)
0653
45.0%
41
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
123
 
0.2%
151
 
0.1%
168
 
0.6%
172
 
0.1%
185
 
0.3%
ValueCountFrequency (%)
5471
0.1%
5231
0.1%
5021
0.1%
4181
0.1%
4061
0.1%
3641
0.1%
3411
0.1%
3191
0.1%
3122
0.1%
3041
0.1%

EnclosedPorch
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct119
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95244659
Minimum0
Maximum552
Zeros1244
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:47.594286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.10822349
Coefficient of variation (CV)2.783663463
Kurtosis10.47448484
Mean21.95244659
Median Absolute Deviation (MAD)0
Skewness3.094001094
Sum31853
Variance3734.214978
MonotonicityNot monotonic
2022-08-14T18:38:47.657449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01244
85.7%
11215
 
1.0%
966
 
0.4%
1445
 
0.3%
1925
 
0.3%
1205
 
0.3%
2165
 
0.3%
2524
 
0.3%
1164
 
0.3%
1564
 
0.3%
Other values (109)154
 
10.6%
ValueCountFrequency (%)
01244
85.7%
191
 
0.1%
201
 
0.1%
241
 
0.1%
301
 
0.1%
322
 
0.1%
342
 
0.1%
362
 
0.1%
371
 
0.1%
392
 
0.1%
ValueCountFrequency (%)
5521
0.1%
3861
0.1%
3301
0.1%
3181
0.1%
3011
0.1%
2941
0.1%
2931
0.1%
2911
0.1%
2861
0.1%
2801
0.1%

3SsnPorch
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.430737422
Minimum0
Maximum508
Zeros1427
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:47.715751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.40694017
Coefficient of variation (CV)8.571609119
Kurtosis122.8756915
Mean3.430737422
Median Absolute Deviation (MAD)0
Skewness10.2717415
Sum4978
Variance864.7681304
MonotonicityNot monotonic
2022-08-14T18:38:47.769246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
01427
98.3%
1683
 
0.2%
1442
 
0.1%
1802
 
0.1%
2162
 
0.1%
2901
 
0.1%
1531
 
0.1%
961
 
0.1%
231
 
0.1%
1621
 
0.1%
Other values (10)10
 
0.7%
ValueCountFrequency (%)
01427
98.3%
231
 
0.1%
961
 
0.1%
1301
 
0.1%
1401
 
0.1%
1442
 
0.1%
1531
 
0.1%
1621
 
0.1%
1683
 
0.2%
1802
 
0.1%
ValueCountFrequency (%)
5081
0.1%
4071
0.1%
3201
0.1%
3041
0.1%
2901
0.1%
2451
0.1%
2381
0.1%
2162
0.1%
1961
0.1%
1821
0.1%

ScreenPorch
Real number (ℝ≥0)

ZEROS

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.15437629
Minimum0
Maximum480
Zeros1335
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:47.830265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.9175219
Coefficient of variation (CV)3.689859669
Kurtosis18.29903044
Mean15.15437629
Median Absolute Deviation (MAD)0
Skewness4.107410709
Sum21989
Variance3126.769255
MonotonicityNot monotonic
2022-08-14T18:38:47.900051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01335
92.0%
1926
 
0.4%
1205
 
0.3%
2245
 
0.3%
1894
 
0.3%
1804
 
0.3%
1473
 
0.2%
903
 
0.2%
1603
 
0.2%
1443
 
0.2%
Other values (66)80
 
5.5%
ValueCountFrequency (%)
01335
92.0%
401
 
0.1%
531
 
0.1%
601
 
0.1%
631
 
0.1%
801
 
0.1%
903
 
0.2%
951
 
0.1%
991
 
0.1%
1002
 
0.1%
ValueCountFrequency (%)
4801
0.1%
4401
0.1%
4101
0.1%
3961
0.1%
3851
0.1%
3741
0.1%
3221
0.1%
3121
0.1%
2911
0.1%
2882
0.1%

PoolArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.77601654
Minimum0
Maximum738
Zeros1444
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:47.956122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.30121249
Coefficient of variation (CV)14.51764134
Kurtosis221.8663877
Mean2.77601654
Median Absolute Deviation (MAD)0
Skewness14.78206885
Sum4028
Variance1624.187728
MonotonicityNot monotonic
2022-08-14T18:38:48.000795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01444
99.5%
5121
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
4801
 
0.1%
5191
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
01444
99.5%
4801
 
0.1%
5121
 
0.1%
5191
 
0.1%
5551
 
0.1%
5761
 
0.1%
6481
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
7381
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
5191
 
0.1%
5121
 
0.1%
4801
 
0.1%
01444
99.5%

PoolQC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1444 
0.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01444
99.5%
0.07
 
0.5%

Length

2022-08-14T18:38:48.054585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:48.107739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01444
99.5%
0.07
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01458
33.5%
.1451
33.3%
11444
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01458
50.2%
11444
49.8%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01458
33.5%
.1451
33.3%
11444
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01458
33.5%
.1451
33.3%
11444
33.2%

Fence
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1170 
0.0
281 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01170
80.6%
0.0281
 
19.4%

Length

2022-08-14T18:38:48.152620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:48.205450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01170
80.6%
0.0281
 
19.4%

Most occurring characters

ValueCountFrequency (%)
01732
39.8%
.1451
33.3%
11170
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01732
59.7%
11170
40.3%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01732
39.8%
.1451
33.3%
11170
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01732
39.8%
.1451
33.3%
11170
26.9%

MiscFeature
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1397 
0.0
 
54

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01397
96.3%
0.054
 
3.7%

Length

2022-08-14T18:38:48.250959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:48.422120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01397
96.3%
0.054
 
3.7%

Most occurring characters

ValueCountFrequency (%)
01505
34.6%
.1451
33.3%
11397
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01505
51.9%
11397
48.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01505
34.6%
.1451
33.3%
11397
32.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01505
34.6%
.1451
33.3%
11397
32.1%

MiscVal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.75878704
Minimum0
Maximum15500
Zeros1399
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:48.465915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation497.6484672
Coefficient of variation (CV)11.37253797
Kurtosis696.692094
Mean43.75878704
Median Absolute Deviation (MAD)0
Skewness24.40151268
Sum63494
Variance247653.9969
MonotonicityNot monotonic
2022-08-14T18:38:48.518328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
01399
96.4%
40011
 
0.8%
5008
 
0.6%
7005
 
0.3%
4504
 
0.3%
6004
 
0.3%
20004
 
0.3%
12002
 
0.1%
4802
 
0.1%
155001
 
0.1%
Other values (11)11
 
0.8%
ValueCountFrequency (%)
01399
96.4%
541
 
0.1%
3501
 
0.1%
40011
 
0.8%
4504
 
0.3%
4802
 
0.1%
5008
 
0.6%
5601
 
0.1%
6004
 
0.3%
6201
 
0.1%
ValueCountFrequency (%)
155001
 
0.1%
83001
 
0.1%
35001
 
0.1%
25001
 
0.1%
20004
0.3%
14001
 
0.1%
13001
 
0.1%
12002
0.1%
11501
 
0.1%
8001
 
0.1%

MoSold
Real number (ℝ≥0)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.319090283
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:48.574608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.700352621
Coefficient of variation (CV)0.4273324956
Kurtosis-0.3981785029
Mean6.319090283
Median Absolute Deviation (MAD)2
Skewness0.2092573567
Sum9169
Variance7.291904275
MonotonicityNot monotonic
2022-08-14T18:38:48.620480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6253
17.4%
7234
16.1%
5201
13.9%
4141
9.7%
8121
8.3%
3104
7.2%
1089
 
6.1%
1178
 
5.4%
962
 
4.3%
1258
 
4.0%
Other values (2)110
7.6%
ValueCountFrequency (%)
158
 
4.0%
252
 
3.6%
3104
7.2%
4141
9.7%
5201
13.9%
6253
17.4%
7234
16.1%
8121
8.3%
962
 
4.3%
1089
 
6.1%
ValueCountFrequency (%)
1258
 
4.0%
1178
 
5.4%
1089
 
6.1%
962
 
4.3%
8121
8.3%
7234
16.1%
6253
17.4%
5201
13.9%
4141
9.7%
3104
7.2%

YrSold
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2009.0
337 
2007.0
327 
2006.0
313 
2008.0
300 
2010.0
174 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8706
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008.0
2nd row2007.0
3rd row2008.0
4th row2006.0
5th row2008.0

Common Values

ValueCountFrequency (%)
2009.0337
23.2%
2007.0327
22.5%
2006.0313
21.6%
2008.0300
20.7%
2010.0174
12.0%

Length

2022-08-14T18:38:48.672901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:48.731169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2009.0337
23.2%
2007.0327
22.5%
2006.0313
21.6%
2008.0300
20.7%
2010.0174
12.0%

Most occurring characters

ValueCountFrequency (%)
04353
50.0%
21451
 
16.7%
.1451
 
16.7%
9337
 
3.9%
7327
 
3.8%
6313
 
3.6%
8300
 
3.4%
1174
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7255
83.3%
Other Punctuation1451
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04353
60.0%
21451
 
20.0%
9337
 
4.6%
7327
 
4.5%
6313
 
4.3%
8300
 
4.1%
1174
 
2.4%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8706
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04353
50.0%
21451
 
16.7%
.1451
 
16.7%
9337
 
3.9%
7327
 
3.8%
6313
 
3.6%
8300
 
3.4%
1174
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04353
50.0%
21451
 
16.7%
.1451
 
16.7%
9337
 
3.9%
7327
 
3.8%
6313
 
3.6%
8300
 
3.4%
1174
 
2.0%

SaleType
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1261 
0.0
190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01261
86.9%
0.0190
 
13.1%

Length

2022-08-14T18:38:48.787020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:48.839668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01261
86.9%
0.0190
 
13.1%

Most occurring characters

ValueCountFrequency (%)
01641
37.7%
.1451
33.3%
11261
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01641
56.5%
11261
43.5%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01641
37.7%
.1451
33.3%
11261
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01641
37.7%
.1451
33.3%
11261
29.0%

SaleCondition
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1.0
1193 
0.0
258 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01193
82.2%
0.0258
 
17.8%

Length

2022-08-14T18:38:48.884906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-14T18:38:48.938284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01193
82.2%
0.0258
 
17.8%

Most occurring characters

ValueCountFrequency (%)
01709
39.3%
.1451
33.3%
11193
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2902
66.7%
Other Punctuation1451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01709
58.9%
11193
41.1%
Other Punctuation
ValueCountFrequency (%)
.1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01709
39.3%
.1451
33.3%
11193
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01709
39.3%
.1451
33.3%
11193
27.4%

SalePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct657
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180624.102
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-08-14T18:38:48.992205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129900
median162500
Q3214000
95-th percentile325812
Maximum755000
Range720100
Interquartile range (IQR)84100

Descriptive statistics

Standard deviation79312.12827
Coefficient of variation (CV)0.4391004711
Kurtosis6.573025006
Mean180624.102
Median Absolute Deviation (MAD)37500
Skewness1.883111046
Sum262085572
Variance6290413691
MonotonicityNot monotonic
2022-08-14T18:38:49.060245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
1.4%
13500017
 
1.2%
14500014
 
1.0%
15500014
 
1.0%
19000013
 
0.9%
11000013
 
0.9%
11500012
 
0.8%
16000012
 
0.8%
13000011
 
0.8%
13900011
 
0.8%
Other values (647)1314
90.6%
ValueCountFrequency (%)
349001
0.1%
353111
0.1%
379001
0.1%
393001
0.1%
400001
0.1%
520001
0.1%
525001
0.1%
550002
0.1%
559931
0.1%
585001
0.1%
ValueCountFrequency (%)
7550001
0.1%
7450001
0.1%
6250001
0.1%
6116571
0.1%
5829331
0.1%
5565811
0.1%
5550001
0.1%
5380001
0.1%
5018371
0.1%
4850001
0.1%

Interactions

2022-08-14T18:38:35.610022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:44.875165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.766494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.525479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.399550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.186069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.957470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.847797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.588621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.474175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.387697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.209438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.099207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.763553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.625891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.363324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.134540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.856550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.653328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.537811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.373959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.249981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.905793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.722518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.537016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.343487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.022855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.893812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.825492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.672995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:44.938529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.828315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.589412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.458295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.244311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.020509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.909348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.649816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.538452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.448003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.271877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.157783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.825369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.683785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.423157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.194099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.916950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.715147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.598780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.437046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.308751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.966027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.783347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.596879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.403741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.084851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.957268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.884593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.736895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.000455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.889580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.651587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.518610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.301993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.084451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.970157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.712950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.601625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.507668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.333492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.217543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.886979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.741876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.481087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.254327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.977657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.778445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.658460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.499481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.367510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.025648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.842986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.656678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.462314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.150388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.019714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.943936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.799598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.064494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.949818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.832039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.577298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.358596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.146636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.030212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.773469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.665123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.566079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.394138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.275681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.948976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.797947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.538555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.313289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.035855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.839050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.718357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.560001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.425361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.084886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.902808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.715625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.520949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.210919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.082507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.001124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.857721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.123232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.007769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.887778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.633202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.412550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.205129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.086686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.831407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.725288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.622254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.452471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.330276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.006408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.850980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.592645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.370062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.089924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.897970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.774532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.618132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.478949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.142514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.958316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.771589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.575492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.268091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.141915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.055802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.917471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.182643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.064639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.945724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.690809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.466305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.262250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.143543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.888436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.785072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.677636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.511288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.383837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.064397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.904150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.647837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.426416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.145305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.955964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.830968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.676272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.533954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.197360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.015996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.826166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.629887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.324459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.202456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.109558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.981822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.247693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.129402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.008248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.753140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.525323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.326712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.206049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.953906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.849499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.740309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.576428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.444792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.128792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.964495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.709107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.489015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.206563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.021495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.893015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.740105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.593813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.259683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.077369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.888016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.690854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.387480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.267092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.170130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.045882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.311799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.190465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.069231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.812098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.583359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.388615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.266435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.137936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.914138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.800162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.638842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.502614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.190665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.020699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.767564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.548140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.380419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.083904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.953937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.801776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.652782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.319824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.138476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.947324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.749994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.563799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.329651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.228535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.109241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.376198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.251945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.130601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.872614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.641596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.452398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.329200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.201363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.977294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.860649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.700543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.561982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.252719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.078466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.826382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.609534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.439459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.147071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.014292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.865192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.710401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.381831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.198889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.008288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.809503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.626426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.392430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.288561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.174055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.441375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.317483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.194584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.934304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.703159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.516487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.393313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.264718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.043665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.923537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.765537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.622458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.433191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.137703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.887059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.672625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.499853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.211190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.077828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.929116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.770621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.444402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.260410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.069093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.870832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.692342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.457648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.349021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.233555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.501591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.376528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.252219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.992118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.759102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.576401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.453686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.325328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.104786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.980478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.825778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.678376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.491484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.191391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.943071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.731498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.556287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.271195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.146738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.989877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.825554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.623667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.316080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.127203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.927058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.752798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.517775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.406098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.297122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.564756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.438550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.327667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.166052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.817880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.639266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.515356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.387473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.170116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.039452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.888093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.737282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.553037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.249027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.001533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.792307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.616035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.334097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.207489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.052730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.885220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.683825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.375947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.187913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.987066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.815172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.581331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.466025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.354940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.623768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.495643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.385787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.219676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.871433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.698586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.571491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.446312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.227679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.095073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.946118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.792140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.608651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.301744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.054963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.849922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.669274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.392268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.263087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.111894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.939875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.739546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.431543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.244416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.041118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.873960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.639716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.520933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.417414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.686413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.556312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.448260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.277347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.930449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.759375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.631551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.507088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.290756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.153137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.007979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.849767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.668920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.357319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.112974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.909931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.726930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.453953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.323146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.173038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.999323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.798337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.490727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.303750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.099046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.935493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.703155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.578307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.473046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.742855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.612135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.502356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.330121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.982263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.816952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.686357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.564319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.460595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.206361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.063456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.903142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.723234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.408366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.164438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.964959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.779670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.511082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.376961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.228654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.051786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.851067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.543037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.357051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.152095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:30.991654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.875947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.631394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.531546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.800346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.668352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.559728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.382965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.035303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.873154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.742565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.621486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.518654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.262232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.121658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:05.957916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.779653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.460131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.217659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.021830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.833467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.683264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.433401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.285257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.105855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.905872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.597717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.410941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.206452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.048735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.934677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.685580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.592597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.862142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.729065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.619360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.439954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.092021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.936160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.802558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.684227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.580190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.320505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.182858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.016557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.840229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.516661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.274776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.080836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.890301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.743320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.491529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.345804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.163733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:23.964493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.655526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.469289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.264074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.109466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:32.997041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.742935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.651011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.921215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.785638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.677171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.493517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.149641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:54.993915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.860674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.741157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.638904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.376237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.242637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.072215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.898698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.569387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.330182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.140070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:14.945946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.800680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.548546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.403426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.218636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.019707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.711116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.524422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.319865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.166855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.057325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.797635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.715519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:45.985473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.848704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.739403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.555120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.209285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.058692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.925648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.806150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.705166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.438938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.305761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.134502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:07.960451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.743500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.390283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.202907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.006272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.864960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.609727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.467905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.277854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.081931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.771432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.585414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.380161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.231952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.134253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.858738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.777597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.047785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.909379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.800311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.612316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.268338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.120297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:56.987288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.866647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.767789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.497290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.367924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.191084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.020878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.799976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.448898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.262326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.065526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.925874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.668489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.528652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.334893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.139983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:25.950053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.644540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.439019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.293394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.199691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.916458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.840151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.111495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:47.971467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.861268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.671811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.328120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.183882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.048063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.930391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.831863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.558121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.430773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.250711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.082328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.857807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.507453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.324749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.126107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:16.989152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.728705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.590790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.393016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.199836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.009913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.703835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.498281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.355825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.263330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:34.975820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.899064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.170438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.029514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.918950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.725554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.499434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.241573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.106264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:58.986288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.890962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.613939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.488563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.305111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.142279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.910489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.562041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.380291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.182115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.046052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.784692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.648259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.447704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.254770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.065151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.757802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.553147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.412502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.322593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.030107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:36.957950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.232026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.089504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:49.976311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.781146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.554576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.302608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.164626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.046127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:00.952283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.789504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.547905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.360251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.200366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:09.964667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.617220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.438362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.239613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.106853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:18.957665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.707886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.503509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.311552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.122001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.815162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.609769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.470967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.383733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.203656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:37.018013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.293233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.151919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.035232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.836684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.610471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.362243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.224142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.104699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.013075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.845860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.607236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.416489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.260160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.019558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.673923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.495202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.298355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.166994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.015191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.766824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.560065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.367689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.179123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.871394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.667229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.529879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.447119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.259204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:37.077315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.353474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.212529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.092334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.893369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.665361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.422956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.281880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.164382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.073354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.904404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.665788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.472848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.318386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.074733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.728282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.553794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.356089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.227975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.072890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.826083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.615613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.424959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.236745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.928235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.723859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.588234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.507526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.315622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:37.138635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.519411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.271465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.153368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:51.949465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.720805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.481972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.340353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.224665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.134266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:02.961937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.725383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.527904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.377302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.130971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.783791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.612364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.414009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.286792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.130254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.885045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.671044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.481139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.294563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:27.984803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.780772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.645805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.567999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.371333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:37.201442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.580248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.337422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.214338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.008478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.778711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.546117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.402083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.288281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.196452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.023117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.788478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.587047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.439232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.189382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.841175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.673976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.474009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.349054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.190719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:20.947743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.729130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.541168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.354826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.046427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.840519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.707068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.633297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.431821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:37.267207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.645121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.401310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.279075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.069119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.841040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.610677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.466949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.352130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.262795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.085854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.853947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.648195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.504154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.248655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:11.903493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.736987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.536438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.416064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.253561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.011701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.790085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.603811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.419029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.109971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.903421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.771013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.700624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.493177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:37.326521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:46.703157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:48.462188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:50.336210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:52.126005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:53.896019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:55.785496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:57.524882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:37:59.412163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:01.322683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:03.147227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:04.912802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:06.703799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:08.562774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:10.304168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:12.074671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:13.794563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:15.592547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:17.474795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:19.310932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:21.070973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:22.845504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:24.660356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:26.476071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:28.283648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:29.960863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:31.830255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:33.760775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-14T18:38:35.549925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-14T18:38:49.181594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-14T18:38:49.765928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-14T18:38:50.355324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-14T18:38:50.922922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-14T18:38:51.457187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-14T18:38:37.628579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-14T18:38:39.116560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-14T18:38:39.561898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexIdMSSubClassMSZoningLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
00.01.060.01.08450.01.01.00.01.01.01.01.01.01.01.01.01.07.05.02003.02003.01.01.01.01.01.0196.01.01.01.01.01.00.01.0706.01.00.0150.0856.01.01.01.01.0856.0854.00.01710.01.00.02.01.03.01.01.08.01.00.00.01.0NaN1.02.0548.01.01.01.00.061.00.00.00.00.01.01.01.00.02.02008.01.01.0208500.0
11.02.020.01.09600.01.01.00.01.01.00.01.00.00.01.01.00.06.08.01976.01976.01.01.00.00.00.00.00.01.00.01.01.00.00.0978.01.00.0284.01262.01.01.01.01.01262.00.00.01262.00.01.02.00.03.01.00.06.01.01.01.01.0NaN1.02.0460.01.01.01.0298.00.00.00.00.00.01.01.01.00.05.02007.01.01.0181500.0
22.03.060.01.011250.01.01.01.01.01.01.01.01.01.01.01.01.07.05.02001.02002.01.01.01.01.01.0162.01.01.01.01.01.01.01.0486.01.00.0434.0920.01.01.01.01.0920.0866.00.01786.01.00.02.01.03.01.01.06.01.01.01.01.0NaN1.02.0608.01.01.01.00.042.00.00.00.00.01.01.01.00.09.02008.01.01.0223500.0
33.04.070.01.09550.01.01.01.01.01.00.01.00.01.01.01.01.07.05.01915.01970.01.01.00.00.00.00.00.01.00.00.00.00.00.0216.01.00.0540.0756.01.00.01.01.0961.0756.00.01717.01.00.01.00.03.01.01.07.01.01.00.00.0NaN0.03.0642.01.01.01.00.035.0272.00.00.00.01.01.01.00.02.02006.01.00.0140000.0
44.05.060.01.014260.01.01.01.01.01.00.01.00.01.01.01.01.08.05.02000.02000.01.01.01.01.01.0350.01.01.01.01.01.00.01.0655.01.00.0490.01145.01.01.01.01.01145.01053.00.02198.01.00.02.01.04.01.01.09.01.01.01.01.0NaN1.03.0836.01.01.01.0192.084.00.00.00.00.01.01.01.00.012.02008.01.01.0250000.0
55.06.050.01.014115.01.01.01.01.01.01.01.00.01.01.01.00.05.05.01993.01995.01.01.01.01.00.00.00.01.00.01.01.00.01.0732.01.00.064.0796.01.01.01.01.0796.0566.00.01362.01.00.01.01.01.01.00.05.01.00.00.01.0NaN0.02.0480.01.01.01.040.030.00.0320.00.00.01.00.00.0700.010.02009.01.01.0143000.0
66.07.020.01.010084.01.01.00.01.01.01.01.00.01.01.01.00.08.05.02004.02005.01.01.01.01.00.0186.01.01.01.00.01.00.01.01369.01.00.0317.01686.01.01.01.01.01694.00.00.01694.01.00.02.00.03.01.01.07.01.01.00.01.0NaN1.02.0636.01.01.01.0255.057.00.00.00.00.01.01.01.00.08.02007.01.01.0307000.0
77.08.060.01.010382.01.01.01.01.01.00.01.00.00.01.01.01.07.06.01973.01973.01.01.00.00.00.0240.00.01.00.01.01.01.00.0859.00.032.0216.01107.01.01.01.01.01107.0983.00.02090.01.00.02.01.03.01.00.07.01.02.01.01.0NaN1.02.0484.01.01.01.0235.0204.0228.00.00.00.01.01.00.0350.011.02009.01.01.0200000.0
88.09.050.00.06120.01.01.00.01.01.01.01.00.00.01.01.00.07.05.01931.01950.01.01.00.00.00.00.00.01.00.00.01.00.00.00.01.00.0952.0952.01.00.01.00.01022.0752.00.01774.00.00.02.00.02.02.00.08.00.02.01.00.0NaN0.02.0468.00.01.01.090.00.0205.00.00.00.01.01.01.00.04.02008.01.00.0129900.0
99.010.0190.01.07420.01.01.00.01.01.00.01.00.00.00.00.00.05.06.01939.01950.01.01.00.00.00.00.00.01.00.00.01.00.01.0851.01.00.0140.0991.01.01.01.01.01077.00.00.01077.01.00.01.00.02.02.00.05.01.02.01.01.0NaN1.01.0205.00.01.01.00.04.00.00.00.00.01.01.01.00.01.02008.01.01.0118000.0

Last rows

df_indexIdMSSubClassMSZoningLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
14411450.01451.090.01.09000.01.01.00.01.01.00.01.00.01.01.00.01.05.05.01974.01974.01.01.01.01.00.00.00.01.00.01.01.00.00.00.01.00.0896.0896.01.00.01.01.0896.0896.00.01792.00.00.02.02.04.02.00.08.01.00.00.00.00.00.00.00.00.00.01.032.045.00.00.00.00.01.01.01.00.09.02009.01.01.0136000.0
14421451.01452.020.01.09262.01.01.00.01.01.01.01.00.01.01.01.00.08.05.02008.02009.01.01.00.00.00.0194.01.01.01.01.01.00.00.00.01.00.01573.01573.01.01.01.01.01578.00.00.01578.00.00.02.00.03.01.00.07.01.01.00.01.0NaN0.03.0840.01.01.01.00.036.00.00.00.00.01.01.01.00.05.02009.00.00.0287090.0
14431452.01453.0180.00.03675.01.01.00.01.01.01.01.00.01.01.00.00.05.05.02005.02005.01.01.01.01.01.080.00.01.01.01.01.00.01.0547.01.00.00.0547.01.00.01.01.01072.00.00.01072.01.00.01.00.02.01.00.05.01.00.00.00.0NaN0.02.0525.01.01.01.00.028.00.00.00.00.01.01.01.00.05.02006.01.01.0145000.0
14441453.01454.020.01.017217.01.01.00.01.01.01.01.00.01.01.01.00.05.05.02006.02006.01.01.01.01.00.00.00.01.01.01.01.00.00.00.01.00.01140.01140.01.01.01.01.01140.00.00.01140.00.00.01.00.03.01.00.06.01.00.00.00.00.00.00.00.00.00.01.036.056.00.00.00.00.01.01.01.00.07.02006.01.00.084500.0
14451454.01455.020.00.07500.01.00.00.01.01.01.01.00.01.01.01.00.07.05.02004.02005.01.01.01.01.00.00.01.01.01.01.01.00.01.0410.01.00.0811.01221.01.01.01.01.01221.00.00.01221.01.00.02.00.02.01.01.06.01.00.00.01.0NaN1.02.0400.01.01.01.00.0113.00.00.00.00.01.01.01.00.010.02009.01.01.0185000.0
14461455.01456.060.01.07917.01.01.00.01.01.01.01.00.01.01.01.01.06.05.01999.02000.01.01.01.01.00.00.00.01.01.01.01.00.00.00.01.00.0953.0953.01.01.01.01.0953.0694.00.01647.00.00.02.01.03.01.00.07.01.01.01.01.0NaN1.02.0460.01.01.01.00.040.00.00.00.00.01.01.01.00.08.02007.01.01.0175000.0
14471456.01457.020.01.013175.01.01.00.01.01.01.01.00.01.01.01.00.06.06.01978.01988.01.01.00.00.00.0119.00.01.00.01.01.00.00.0790.00.0163.0589.01542.01.00.01.01.02073.00.00.02073.01.00.02.00.03.01.00.07.00.02.01.01.0NaN0.02.0500.01.01.01.0349.00.00.00.00.00.01.00.01.00.02.02010.01.01.0210000.0
14481457.01458.070.01.09042.01.01.00.01.01.01.01.00.01.01.01.01.07.09.01941.02006.01.01.00.00.00.00.00.00.00.00.00.00.01.0275.01.00.0877.01152.01.01.01.01.01188.01152.00.02340.00.00.02.00.04.01.01.09.01.02.00.01.0NaN1.01.0252.01.01.01.00.060.00.00.00.00.01.00.00.02500.05.02010.01.01.0266500.0
14491458.01459.020.01.09717.01.01.00.01.01.01.01.00.01.01.01.00.05.06.01950.01996.00.01.00.00.00.00.00.01.00.00.01.01.01.049.00.01029.00.01078.01.00.01.00.01078.00.00.01078.01.00.01.00.02.01.01.05.01.00.00.01.0NaN0.01.0240.01.01.01.0366.00.0112.00.00.00.01.01.01.00.04.02010.01.01.0142125.0
14501459.01460.020.01.09937.01.01.00.01.01.01.01.00.01.01.01.00.05.06.01965.01965.01.01.00.00.00.00.01.01.00.00.01.00.00.0830.00.0290.0136.01256.01.00.01.01.01256.00.00.01256.01.00.01.01.03.01.00.06.01.00.00.01.0NaN0.01.0276.01.01.01.0736.068.00.00.00.00.01.01.01.00.06.02008.01.01.0147500.0